SmartShift: Expanded Load Shifting Incentive Mechanism for Risk-Averse Consumers
Authors: Bochao Shen, Balakrishnan Narayanaswamy, Ravi Sundaram
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | demonstration of the practical benefits of Smart Shift through extensive simulations (Simulations section).We evaluate Smart Shift using a real-time electricity load data set from Smart* (Barker et al. 2012). Through numerical experiments, we then study how our expanded load shifting mechanism can improve the performance over flat-rate pricing while providing a win-win solution to both the distribution company as well as the consumer. |
| Researcher Affiliation | Academia | Bochao Shen CCIS Northeastern University Boston MA, 02115 ordinary@ccs.neu.edu Balakrishnan Narayanaswamy Computer Science and Engineering University of California, San Diego La Jolla CA, 92093 muralib@cs.ucsd.edu Ravi Sundaram CCIS Northeastern University Boston MA, 02115 koods@ccs.neu.edu |
| Pseudocode | Yes | Algorithm 2 Optimal load shifting under exogenous market price Input: {x(t) i |1 i n, 1 t k}, {m(s t) i |1 i n, 1 s k, 1 t k}. Output: {z(t) i |1 i n, 1 t k}. 1: for each consumer i do 2: for each time slot t do 3: z(t) i arg min 1 t k {m(t t ) i p(t ) m }; 4: end for 5: end for |
| Open Source Code | No | No explicit statement or link indicating the availability of open-source code for the described methodology was found. |
| Open Datasets | Yes | We evaluate Smart Shift using a real-time electricity load data set from Smart* (Barker et al. 2012). |
| Dataset Splits | No | The paper describes data processing and filtering ('average the (per minute) sampled data points per hour', 'filter out households with zero power usage'), but does not provide explicit training/validation/test dataset splits, percentages, or absolute sample counts. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or cloud instance types) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions simulating market prices using a normal distribution, but does not list any specific software components or libraries with version numbers (e.g., Python 3.8, PyTorch 1.9, CPLEX 12.4). |
| Experiment Setup | Yes | In all our simulations, each point is averaged over 100 repeated experiments. 95% confidence interval is also shown. In Figure 1, we draw the risk-aversion parameter {λi} from the Pareto distribution, Eq. (2), by setting βR = 1 and λmin = 1. These are fixed for subsequent repetitions. ... In Figure 2, we fix the fluctuation of the price by setting µm = 50, σm = 5, but vary the tolerance of the consumers by varying the βG from 0.5 to 5 with step size of 0.5. For each βG, we sample a set of prices from normal distribution N(µm, σ2 m) where µ = 50, σm = 5, then sample the {m(s t) i } from Eq. (3) with the fixed {m(s t) i,min} and the varying βG. |